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A neural network approach to local downscaling of GCM output for assessing wind power implications of climate change

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  • Sailor, D.J
  • Hu, T
  • Li, X
  • Rosen, J.N

Abstract

A methodology is presented for downscaling General Circulation Model (GCM) output to predict surface wind speeds at scales of interest in the wind power industry under expected future climatic conditions. The approach involves a combination of Neural Network tools and traditional weather forecasting techniques. A Neural Network transfer function is developed to relate local wind speed observations to large scale GCM predictions of atmospheric properties under current climatic conditions. By assuming the invariability of this transfer function under conditions of doubled atmospheric carbon dioxide, the resulting transfer function is then applied to GCM output for a transient run of the National Center for Atmospheric Research coupled ocean-atmosphere GCM. This methodology is applied to three test sites in regions relevant to the wind power industry—one in Texas and two in California. Changes in daily mean wind speeds at each location are presented and discussed with respect to potential implications for wind power generation.

Suggested Citation

  • Sailor, D.J & Hu, T & Li, X & Rosen, J.N, 2000. "A neural network approach to local downscaling of GCM output for assessing wind power implications of climate change," Renewable Energy, Elsevier, vol. 19(3), pages 359-378.
    • Handle: RePEc:eee:renene:v:19:y:2000:i:3:p:359-378
    DOI: 10.1016/S0960-1481(99)00056-7
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    Citations

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    Cited by:

    1. Jane Ebinger & Walter Vergara, 2011. "Climate Impacts on Energy Systems : Key Issues for Energy Sector Adaptation," World Bank Publications - Books, The World Bank Group, number 2271, December.
    2. Schaeffer, Roberto & Szklo, Alexandre Salem & Pereira de Lucena, André Frossard & Moreira Cesar Borba, Bruno Soares & Pupo Nogueira, Larissa Pinheiro & Fleming, Fernanda Pereira & Troccoli, Alberto & , 2012. "Energy sector vulnerability to climate change: A review," Energy, Elsevier, vol. 38(1), pages 1-12.
    3. De Giorgi, Maria Grazia & Ficarella, Antonio & Tarantino, Marco, 2011. "Assessment of the benefits of numerical weather predictions in wind power forecasting based on statistical methods," Energy, Elsevier, vol. 36(7), pages 3968-3978.
    4. Segal, Moti & Pan, Zaitao & Arritt, Raymond W & Takle, Eugene S, 2001. "On the potential change in wind power over the US due to increases of atmospheric greenhouse gases," Renewable Energy, Elsevier, vol. 24(2), pages 235-243.
    5. Breslow, Paul B. & Sailor, David J., 2002. "Vulnerability of wind power resources to climate change in the continental United States," Renewable Energy, Elsevier, vol. 27(4), pages 585-598.
    6. Soukissian, Takvor H. & Papadopoulos, Anastasios, 2015. "Effects of different wind data sources in offshore wind power assessment," Renewable Energy, Elsevier, vol. 77(C), pages 101-114.
    7. De Giorgi, Maria Grazia & Ficarella, Antonio & Tarantino, Marco, 2011. "Error analysis of short term wind power prediction models," Applied Energy, Elsevier, vol. 88(4), pages 1298-1311, April.

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